Relaxing state-access constraints in stateful programmable data planes

Supporting the programming of stateful packet forwarding functions in hardware has recently attracted the interest of the research community. When designing such switching chips, the challenge is to guarantee the ability to program functions that can read and modify data plane's state, while keeping line rate performance and state consistency. Current state-of-the-art designs are based on a very conservative all-or-nothing model: programmability is limited only to those functions that are guaranteed to sustain line rate, with any traffic workload. In effect, this limits the maximum time to execute state update operations. In this paper, we explore possible options to relax these constraints by using simulations on real traffic traces. We then propose a model in which functions can be executed in a larger but bounded time, while preventing data hazards with memory locking. We present results showing that such flexibility can be supported with little or no throughput degradation.Comment: 6 page

Traffic Optimization in Data Center and Software-Defined Programmable Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

Stateful Data Plane Abstractions for Software-Defined Networks and Their Applications

RESUMÉ Le Software-Defined Networking (SDN) permet la programmation du réseau. Malheureusement, la technologie SDN actuelle limite la programmabilité uniquement au plan de contrôle. Les opérateurs ne peuvent pas programmer des algorithmes du plan de données tels que l’équilibrage de charge, le contrôle de congestion, la détection de pannes, etc. Ces fonctions sont implémentées à l’aide d’hardware dédié, car elles doivent fonctionner au taux de ligne, c’est-à-dire 10-100 Gbit/s sur 10-100 ports. Dans ce travail, nous présentons deux abstractions de plan de données pour le traitement de paquets à états (stateful), OpenState et OPP. OpenState est une extension d’OpenFlow qui permet la définition des règles de flux en tant que machines à états finis. OPP est une abstraction plus flexible qui généralise OpenState en ajoutant des capacités de calcul, permettant la programmation d’algorithmes de plan de données plus avancés. OpenState et OPP sont à la fois disponibles pour les implémentations d’haute performance en utilisant des composants de commutateurs hardware courants. Cependant, les deux abstractions sont basées sur un choix de design problématique : l’utilisation d’une boucle de rétroaction dans le pipeline de traitement des paquets. Cette boucle, si elle n’est pas correctement contrôlée, peut nuire à la cohérence des opérations d’état. Les approches de verrouillage de la mémoire peuvent être utilisées pour éviter les incohérences, au détriment du débit. Nous présentons des résultats de simulations sur des traces de trafic réelles, montrant que les boucles de rétroaction de plusieurs cycles d’horloge peuvent être supportées avec peu ou pas de dégradation des performances, même avec les charges de travail des plus défavorables. Pour mieux prouver les avantages d’un plan de données programmables, nous présentons deux nouvelles applications : Spider et FDPA. Spider permet de détecter et de réagir aux pannes de réseau aux échelles temporelles du plan de données (i.e., micro/nanosecondes), également dans le cas de pannes à distance. En utilisant OpenState, Spider fournit des fonctionnalités équivalentes aux protocoles de plans de contrôle anciens tels que BFD et MPLS Fast Reroute, mais sans nécessiter un plan de contrôle.---------- ABSTRACT Software-Defined Networking (SDN) enables programmability in the network. Unfortunately, current SDN limits programmability only to the control plane. Operators cannot program data plane algorithms such as load balancing, congestion control, failure detection, etc. These capabilities are usually baked in the switch via dedicated hardware, as they need to run at line rate, i.e. 10-100 Gbit/s on 10-100 ports. In this work, we present two data plane abstractions for stateful packet processing, namely OpenState and OPP. These abstractions allow operators to program data plane tasks that involve stateful processing. OpenState is an extension to OpenFlow that permits the definition of forwarding rules as finite state machines. OPP is a more flexible abstraction that generalizes OpenState by adding computational capabilities, opening for the programming of more advanced data plane algorithms. Both OpenState and OPP are amenable for highperformance hardware implementations by using commodity hardware switch components. However, both abstractions are based on a problematic design choice: to use a feedback-loop in the processing pipeline. This loop, if not adequately controlled, can represent a harm for the consistency of the state operations. Memory locking approaches can be used to prevent inconsistencies, at the expense of throughput. We present simulation results on real traffic traces showing that feedback-loops of several clock cycles can be supported with little or no performance degradation, even with near-worst case traffic workloads. To further prove the benefits of a stateful programmable data plane, we present two novel applications: Spider and FDPA. Spider permits to detect and react to network failures at data plane timescales, i.e. micro/nanoseconds, also in the case of distant failures. By using OpenState, Spider provides functionalities equivalent to legacy control plane protocols such as BFD and MPLS Fast Reroute, but without the need of a control plane. That is, both detection and rerouting happen entirely in the data plane. FDPA allows a switch to enforce approximate fair bandwidth sharing among many TCP-like senders. Most of the mechanisms to solve this problem are based on complex scheduling algorithms, whose feasibility becomes very expensive with today’s line rate requirements. FDPA, which is based on OPP, trades scheduling complexity with per-user state. FDPA works by dynamically assigning users to few (3-4) priority queues, where the priority is chosen based on the sending rate history of a user

Improving software middleboxes and datacenter task schedulers

Over the last decades, shared systems have contributed to the popularity of many technologies. From Operating Systems to the Internet, they have all brought significant cost savings by allowing the underlying infrastructure to be shared. A common challenge in these systems is to ensure that resources are fairly divided without compromising utilization efficiency. In this thesis, we look at problems in two shared systems—software middleboxes and datacenter task schedulers—and propose ways of improving both efficiency and fairness. We begin by presenting Sprayer, a system that uses packet spraying to load balance packets to cores in software middleboxes. Sprayer eliminates the imbalance problems of per-flow solutions and addresses the new challenges of handling shared flow state that come with packet spraying. We show that Sprayer significantly improves fairness and seamlessly uses the entire capacity, even when there is a single flow in the system. After that, we present Stateful Dominant Resource Fairness (SDRF), a task scheduling policy for datacenters that looks at past allocations and enforces fairness in the long run. We prove that SDRF keeps the fundamental properties of DRF—the allocation policy it is built on—while benefiting users with lower usage. To efficiently implement SDRF, we also introduce live tree, a general-purpose data structure that keeps elements with predictable time-varying priorities sorted. Our trace-driven simulations indicate that SDRF reduces users’ waiting time on average. This improves fairness, by increasing the number of completed tasks for users with lower demands, with small impact on high-demand users.Nas últimas décadas, sistemas compartilhados contribuíram para a popularidade de muitas tecnologias. Desde Sistemas Operacionais até a Internet, esses sistemas trouxeram economias significativas ao permitir que a infraestrutura subjacente fosse compartilhada. Um desafio comum a esses sistemas é garantir que os recursos sejam divididos de forma justa, sem comprometer a eficiência de utilização. Esta dissertação observa problemas em dois sistemas compartilhados distintos—middleboxes em software e escalonadores de tarefas de datacenters—e propõe maneiras de melhorar tanto a eficiência como a justiça. Primeiro é apresentado o sistema Sprayer, que usa espalhamento para direcionar pacotes entre os núcleos em middleboxes em software. O Sprayer elimina os problemas de desbalanceamento causados pelas soluções baseadas em fluxos e lida com os novos desafios de manipular estados de fluxo, consequentes do espalhamento de pacotes. É mostrado que o Sprayer melhora a justiça de forma significativa e consegue usar toda a capacidade, mesmo quando há apenas um fluxo no sistema. Depois disso, é apresentado o SDRF, uma política de alocação de tarefas para datacenters que considera as alocações passadas e garante justiça ao longo do tempo. Prova-se que o SDRF mantém as propriedades fundamentais do DRF—a política de alocação em que ele se baseia—enquanto beneficia os usuários com menor utilização. Para implementar o SDRF de forma eficiente, também é introduzida a árvore viva, uma estrutura de dados genérica que mantém ordenados elementos cujas prioridades variam com o tempo. Simulações com dados reais indicam que o SDRF reduz o tempo de espera na média. Isso melhora a justiça, ao aumentar o número de tarefas completas dos usuários com menor demanda, tendo um impacto pequeno nos usuários de maior demanda

Accurate and Resource-Efficient Monitoring for Future Networks

Monitoring functionality is a key component of any network management system. It is essential for profiling network resource usage, detecting attacks, and capturing the performance of a multitude of services using the network. Traditional monitoring solutions operate on long timescales producing periodic reports, which are mostly used for manual and infrequent network management tasks. However, these practices have been recently questioned by the advent of Software Defined Networking (SDN). By empowering management applications with the right tools to perform automatic, frequent, and fine-grained network reconfigurations, SDN has made these applications more dependent than before on the accuracy and timeliness of monitoring reports. As a result, monitoring systems are required to collect considerable amounts of heterogeneous measurement data, process them in real-time, and expose the resulting knowledge in short timescales to network decision-making processes. Satisfying these requirements is extremely challenging given today’s larger network scales, massive and dynamic traffic volumes, and the stringent constraints on time availability and hardware resources. This PhD thesis tackles this important challenge by investigating how an accurate and resource-efficient monitoring function can be realised in the context of future, software-defined networks. Novel monitoring methodologies, designs, and frameworks are provided in this thesis, which scale with increasing network sizes and automatically adjust to changes in the operating conditions. These achieve the goal of efficient measurement collection and reporting, lightweight measurement- data processing, and timely monitoring knowledge delivery

Control plane optimization in Software Defined Networking and task allocation for Fog Computing

As the next generation of mobile wireless standard, the fifth generation (5G) of cellular/wireless network has drawn worldwide attention during the past few years. Due to its promise of higher performance over the legacy 4G network, an increasing number of IT companies and institutes have started to form partnerships and create 5G products. Emerging techniques such as Software Defined Networking and Mobile Edge Computing are also envisioned as key enabling technologies to augment 5G competence. However, as popular and promising as it is, 5G technology still faces several intrinsic challenges such as (i) the strict requirements in terms of end-to-end delays, (ii) the required reliability in the control plane and (iii) the minimization of the energy consumption. To cope with these daunting issues, we provide the following main contributions. As first contribution, we address the problem of the optimal placement of SDN controllers. Specifically, we give a detailed analysis of the impact that controller placement imposes on the reactivity of SDN control plane, due to the consistency protocols adopted to manage the data structures that are shared across different controllers. We compute the Pareto frontier, showing all the possible tradeoffs achievable between the inter-controller delays and the switch-to-controller latencies. We define two data-ownership models and formulate the controller placement problem with the goal of minimizing the reaction time of control plane, as perceived by a switch. We propose two evolutionary algorithms, namely Evo-Place and Best-Reactivity, to compute the Pareto frontier and the controller placement minimizing the reaction time, respectively. Experimental results show that Evo-Place outperforms its random counterpart, and Best-Reactivity can achieve a relative error of <= 30% with respect to the optimal algorithm by only sampling less than 10% of the whole solution space. As second contribution, we propose a stateful SDN approach to improve the scalability of traffic classification in SDN networks. In particular, we leverage the OpenState extension to OpenFlow to deploy state machines inside the switch and minimize the number of packets redirected to the traffic classifier. We experimentally compare two approaches, namely Simple Count-Down (SCD) and Compact Count-Down (CCD), to scale the traffic classifier and minimize the flow table occupancy. As third contribution, we propose an approach to improve the reliability of SDN controllers. We implement BeCheck, which is a software framework to detect ``misbehaving'' controllers. BeCheck resides transparently between the control plane and data plane, and monitors the exchanged OpenFlow traffic messages. We implement three policies to detect misbehaving controllers and forward the intercepted messages. BeCheck along with the different policies are validated in a real test-bed. As fourth contribution, we investigate a mobile gaming scenario in the context of fog computing, denoted as Integrated Mobile Gaming (IMG) scenario. We partition mobile games into individual tasks and cognitively offload them either to the cloud or the neighbor mobile devices, so as to achieve minimal energy consumption. We formulate the IMG model as an ILP problem and propose a heuristic named Task Allocation with Minimal Energy cost (TAME). Experimental results show that TAME approaches the optimal solutions while outperforming two other state-of-the-art task offloading algorithms

Abstractions and optimisations for model-checking software-defined networks

Software-Defined Networking introduces a new programmatic abstraction layer by shifting the distributed network functions (NFs) from silicon chips (ASICs) to a logically centralized (controller) program. And yet, controller programs are a common source of bugs that can cause performance degradation, security exploits and poor reliability in networks. Assuring that a controller program satisfies the specifications is thus most preferable, yet the size of the network and the complexity of the controller makes this a challenging effort. This thesis presents a highly expressive, optimised SDN model, (code-named MoCS), that can be reasoned about and verified formally in an acceptable timeframe. In it, we introduce reusable abstractions that (i) come with a rich semantics, for capturing subtle real-world bugs that are hard to track down, and (ii) which are formally proved correct. In addition, MoCS deals with timeouts of flow table entries, thus supporting automatic state refresh (soft state) in the network. The optimisations are achieved by (1) contextually analysing the model for possible partial order reductions in view of the concrete control program, network topology and specification property in question, (2) pre-computing packet equivalence classes and (3) indexing packets and rules that exist in the model and bit-packing (compressing) them. Each of these developments is demonstrated by a set of real-world controller programs that have been implemented in network topologies of varying size, and publicly released under an open-source license

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing